Remove AI Watermarks

Remove AI provenance marks from images and video you generated yourself:

• known visible labels such as the Google Gemini sparkle watermark and vendor text marks;
• invisible pixel watermarks through direct local-format disruption or diffusion regeneration;
• C2PA, EXIF, XMP, IPTC, and related AI metadata.

uv tool install "remove-ai-watermarks[visible]"   # CLI for visible marks and metadata
npx skills add wiltodelta/remove-ai-watermarks    # agent skill for Claude Code, Codex, Cursor, and other agents

Video support covers provenance identification, complete visible-plus-metadata cleaning, directory batches, visible Sora, Veo, Seedance, Doubao, Dola, Hailuo AI, and Kling AI mark removal, and oracle-certified VAE regeneration for video SynthID removal.

raiw.cc runs this library as a hosted service, with the GPU included and nothing to install. Visible mark and metadata removal at Standard output up to 12 MP are free there; original resolution above 12 MP and invisible watermark removal are paid.

skills.sh

This is a research project on AI provenance signals, for lawful use on content you own. It is provided as is, without warranty or liability, and each user is responsible for how they use it. It does not target stock agency previews or other watermarks that protect third party paid content. Some jurisdictions, including China, restrict removing AI labels or supplying tools that do. See scope, safety, and legal notes.

Choose what you want to do

Microsoft Paint and Photos InvisMark declarations are routed automatically to pixel regeneration. The all command removes both the hidden pixel watermark and its linked C2PA manifest; metadata stripping alone removes only the manifest. A specialized Python API can inspect and disrupt the validated local Watermarker.dll payload without diffusion.

Installation modes

Lower-level and specialized extras include pixels, heif, trustmark, migan, lama, diffusion, classify-onnx, and source-classify. The installation guide documents their exact dependency composition, Python compatibility, and model requirements.

Quick start

Install the metadata-focused default CLI:

uv tool install remove-ai-watermarks

Inspect an image:

remove-ai-watermarks identify image.png

To classify a photograph from pixels (AI versus camera, optional provider), install the extra and call classify. identify never starts it:

uv tool install --force "remove-ai-watermarks[classify]"
remove-ai-watermarks classify image.png

Guide: photo pixel classification.

For a lightweight, abstaining OpenAI/Google/unknown source-export signal after metadata removal, use the separate Python API. It is not a SynthID detector:

import remove_ai_watermarks as raiw

result = raiw.classify_source("image.png")
print(result.label, result.reason)

Install remove-ai-watermarks[source-classify]. Guide: source-pipeline classification.

Signed provenance is the supported route for SynthID and identify reads it. There is no local SynthID pixel detector in the package. Research on a periodic lattice expert is in scripts/synthid_runtime/ and synthid-detector-research.md.

For visible watermark removal, install the pixel dependencies:

uv tool install --force "remove-ai-watermarks[visible]"

Then remove a known visible mark and AI metadata:

remove-ai-watermarks visible image.png -o clean.png

Strip metadata without running visible inpainting or diffusion:

remove-ai-watermarks metadata image.png --remove -o clean.png

Without -o this command overwrites the source in place.

Inspect or remove AI metadata from an MP4, MOV, M4V, WebM, MKV, AVI, or FLV file:

remove-ai-watermarks video metadata input.mp4 --check
remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4

The video metadata command does not transcode video or audio streams. Unlike the image command above, when -o is omitted it writes <source>_clean and preserves the original. MP4 and MOV inspection includes the native TC260 AIGC tag in moov.udta.meta.keys/ilst, including a moov placed after the media payload, plus the QuickTime-form meta variants Doubao's iOS export writes (a bare meta box as a direct moov child, and a keyless hdlr=mdir metadata list). Keyed workflow and prompt entries in the same metadata-list structure are also detected and removed, including ComfyUI exports. MKV and WebM inspection reads the normative Segment.Tags.Tag.SimpleTag placement. AVI uses LIST/INFO/AIGC, while FLV uses script.onMetaData.AIGC. The non-ISOBMFF formats are remuxed with stream copy for removal.

Use the product-oriented video path to identify or clean a file:

uv tool install --force "remove-ai-watermarks[video]"
remove-ai-watermarks video identify input.mp4
remove-ai-watermarks video all input.mp4 -o clean.mp4

video all removes a stable registered visible mark when present and always strips verified AI metadata. If neither signal is found, it still writes a same-container passthrough, so application callers get one predictable output contract. Proprietary invisible-video removal is excluded by default. --invisible opts into the lossy, oracle-certified video SynthID profile.

Process a directory with the same contract:

remove-ai-watermarks video batch ./videos --mode all

Remove a supported visible video mark:

remove-ai-watermarks video visible input.mp4 -o clean.mp4
remove-ai-watermarks video visible veo.mp4 --mark veo -o veo_clean.mp4
remove-ai-watermarks video visible seedance.mp4 --mark seedance -o seedance_clean.mp4
remove-ai-watermarks video visible dola.mp4 --mark dola -o dola_clean.mp4
remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4
remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4

This path scans the complete sequence before changing pixels. It accepts only a mark that repeats at a stable position across adjacent frames, then reuses the same OpenCV, MI-GAN, or LaMa fill backends as image removal. Audio is copied without re-encoding and is allowed to reach its natural end; the video stream is transcoded because its pixels change. By default, a guarded optical-flow pass motion-aligns the preceding accepted fill and blends it only when the nearby source context agrees; use --no-temporal-consistency to disable it. The encoder preserves supported 8-bit source chroma sampling, color tags, and MP4/MOV track timescale instead of relying on ffmpeg's implicit raw-BGR defaults. Variable frame intervals are preserved through a timestamped in-memory NUT bridge instead of being flattened to the average frame rate. Non-zero source start timestamps are retained together with the copied audio offset. The default --mark auto scans all providers in one decode pass and selects the first stable match in the specificity order shown below. Valid Google AI-video provenance breaks a Sora/Veo cross-match in favor of Veo when the diamond is nested in the Sora region or Veo persists through the complete clip while Sora does not. Pass an explicit mark to restrict detection to one provider. Sora covers the moving Sora 2 mascot, with or without the wordmark; mascot-only matches also require the complete two-eye shape. Veo covers both the current four-point diamond and the legacy Veo text. Seedance covers the fixed boxed AI label, Dola covers the fixed Dola AI text, Hailuo AI covers the composite MINIMAX | hailuo AI label, and Kling AI covers the bottom-right KLING AI or KlingAI label with its version suffix. A completed encode is published atomically. No output is written when no stable mark is found. HDR, PQ/HLG, and greater-than-8-bit inputs are rejected before encoding rather than silently reduced through OpenCV's 8-bit BGR boundary.

Apply the calibrated video-pixel SynthID-removal profile:

uv tool install --force "remove-ai-watermarks[video,diffusion]"
remove-ai-watermarks video invisible input.mp4 -o clean.mp4

This path regenerates the complete sequence with one latent-noise field shared across time, copies complete audio, strips source metadata, and publishes the completed encode atomically. The default noise_std=0.15 profile passed both the two-carrier calibration and a complete public eight-second Veo oracle check. Google does not publish a local decoder, so a fresh provider check remains useful for unusually important files or after provider changes, but it is not a product result state. The CLI therefore describes video-pixel regeneration instead of claiming a per-file SynthID verdict, and reports the copied audio watermark as UNVERIFIED.

For invisible watermark removal through the CLI or high-level API, install the qwen-zimage extra. An NVIDIA GPU is required: all diffusion profiles are CUDA-only, and there is no CPU or MPS fallback. The specialized local Paint API uses the pixels extra without CUDA.

uv tool install --force "remove-ai-watermarks[qwen-zimage]"
remove-ai-watermarks invisible image.png -o clean.png

If the local detectors cannot confirm an invisible watermark but you know the image came from an AI generator, add --force:

remove-ai-watermarks invisible image.png -o clean.png --force

Typography-heavy images can opt into the experimental verified-text post-pass. It accepts manually reviewed strings and line boxes or an operator-verified geometry-only manifest; it never treats raw OCR output as ground truth or runs automatically:

uv tool install --force "remove-ai-watermarks[text-restoration]"
remove-ai-watermarks invisible image.png -o clean.png \
  --text-manifest verified-lines.json --force

See the CLI guide for the manifest schema, compatibility restrictions, and oracle caveats.

See the installation guide for Homebrew, uv, optional features, and development setup.

Examples

Visible Gemini mark

The after raster at the top of this page is generated from the tracked before raster by the public path:

uv run remove-ai-watermarks visible demo_banana_before.png \
  --backend cv2 -o demo_banana_after.png

High quality invisible removal

qwen-zimage is the default profile: a Qwen-Image-2512 Lightning pass under Canny ControlNet, followed by SAM-masked Z-Image repair of any detected face. The alternative, sdxl-zimage, swaps the global stage for SDXL and keeps the same face stage. A third profile, chroma-zimage, uses the Apache-2.0 Chroma1 global pass with its own flat vendor floors; see docs/chroma1-engine-research.md for the calibration. --pipeline auto picks sdxl-zimage for Google, chroma-zimage for Microsoft, and qwen-zimage otherwise. All are CUDA only.

uv tool install --force "remove-ai-watermarks[qwen-zimage]"
remove-ai-watermarks invisible image.png -o clean.png --force

OpenAI example before	OpenAI example after
OpenAI portrait grid before qwen-zimage

These exact output files were checked with the matching provider verifiers. That result applies to these files, not to every seed, image, or future watermark version.

Common recipes

Remove every detected visible mark

remove-ai-watermarks visible image.png -o clean.png

The default --mark auto checks all registered visible marks and removes every match. If the mark is visible to you but the detector misses it, select its region explicitly:

remove-ai-watermarks erase image.png \
  --region 1640,1930,400,100 \
  -o clean.png

--region uses x,y,width,height and may be repeated.

Use a learned fill backend

The visible extra uses OpenCV inpainting when no learned backend is installed. For more difficult backgrounds, the learned-backend extras include the same pixel dependencies automatically:

uv tool install --force "remove-ai-watermarks[migan]"
remove-ai-watermarks visible image.png -o clean.png --backend migan

uv tool install --force "remove-ai-watermarks[lama]"
remove-ai-watermarks visible image.png -o clean.png --backend lama

Reduce CUDA memory use

remove-ai-watermarks invisible image.png -o clean.png \
  --cpu-offload --force

CPU offload lowers CUDA memory pressure by moving model components between CPU and GPU, at the cost of speed.

Process a directory

remove-ai-watermarks batch ./images --mode visible
remove-ai-watermarks batch ./images --mode all

What the tool can recognize

Visible mark support includes:

• Google Gemini and Nano Banana visible sparkle watermark;
• Doubao, the Jimeng wordmark and top-left AI生成 layouts, Qwen, Kling AI, Yuanbao, Dola, WorkBuddy, Baidu, LiblibAI's bottom-center wordmark and compact top-left pill, and RunningHub labels;
• calibrated Microsoft top-right white and beige AI-badge layouts;
• calibrated Italian and Korean Samsung Galaxy AI disclosures;
• a generic brand-less bottom-right AI生成 fallback for OS/gallery-level AI-edit stamps that carry no vendor wordmark of their own (vivo and Xiaomi Gallery confirmed); synthetic-only calibration, weaker evidence than the vendor-tuned detectors above;
• the OpenArt wordmark, placed at the frame center rather than a corner; uncalibrated (one confirmed real case, no captured corpus).

Metadata and provenance inspection covers C2PA, EXIF, XMP, IPTC, common generator parameters in image and video containers, China TC260 AIGC labels, and several vendor specific signals. Optional decoders add support for open DWT-DCT watermarks and Adobe TrustMark.

The exact support matrix, including important locale and detector limits, lives in supported signals. The synthetic example gallery carries one canonical sample for every registered image and video mark.

How it works

Visible removal follows three steps:

1. Detect a registered mark in its expected area.
2. Build a mask around the mark.
3. Fill only the masked region with OpenCV, MI-GAN, or LaMa.

Metadata removal uses format aware stripping. JPEG metadata removal preserves the encoded image scan instead of recompressing it. Native MP4/MOV TC260 values and keyed generation parameters are blanked without changing box sizes or media offsets. Other supported containers use their corresponding metadata path.

Invisible removal through the CLI and high-level API regenerates the image through a diffusion pipeline. Specialized callers can instead decode and disrupt the positively identified local Microsoft Paint format with much smaller pixel changes. Neither path can guarantee that every proprietary verifier will reject every output.

See supported signals and known limitations for the full technical boundary.

Python API

The visible-removal API requires remove-ai-watermarks[visible].

import remove_ai_watermarks as raiw

result, removed = raiw.remove_visible("watermarked.png", "clean.png")
print(removed)

report = raiw.remove_visible_detailed("watermarked.png", "clean.png")
print(report.status)  # cleaned | partial | unvalidated | no_watermark

complete = raiw.remove_all("watermarked.png", "clean.png")
print(complete.visible_status, complete.visible_marks)

images = raiw.remove_batch("images", "images_clean", mode="visible")
for item in images.items:
    print(item.source, item.visible_status, item.visible_marks)

provenance = raiw.identify_video("input.mp4")
report = raiw.inspect_video_metadata("input.mp4")
complete = raiw.remove_video_all("input.mp4", "clean.mp4")
print(complete.visual_invisible_action, complete.audio.watermark_status)
batch = raiw.remove_video_batch("videos", "videos_clean")
cleaned = raiw.remove_video_metadata("input.mp4")
synthid_cleaned = raiw.remove_video_invisible("input.mp4", "synthid_clean.mp4")
print(synthid_cleaned.visual_invisible_action, synthid_cleaned.audio.watermark_status)
visible = raiw.remove_video_visible("input.mp4", "clean.mp4")
print(visible.mark)
veo = raiw.remove_video_visible("veo.mp4", "veo_clean.mp4", mark="veo")
seedance = raiw.remove_video_visible(
    "seedance.mp4",
    "seedance_clean.mp4",
    mark="seedance",
)
dola = raiw.remove_video_visible("dola.mp4", "dola_clean.mp4", mark="dola")

The high level API accepts a file path or a BGR NumPy array. For path inputs it also reads provenance metadata, preserves alpha, and can strip AI metadata from the written result.

See the Python API guide for visible removal, the full remove_all and remove_batch pipeline, provenance inspection, metadata stripping, and diffusion usage.

Agent skill

Coding agents can install the published skill and drive this CLI:

npx skills add wiltodelta/remove-ai-watermarks

Claude Code can add this repository as a plugin marketplace:

/plugin marketplace add wiltodelta/remove-ai-watermarks
/plugin install remove-ai-watermarks@remove-ai-watermarks

Codex reads the same skill through .codex-plugin/plugin.json. The skill refuses stock-agency and other third-party paid-asset marks. See the agent skill guide.

MCP server

remove-ai-watermarks mcp serves identify, remove and batch as Model Context Protocol tools over stdio, each a thin wrapper around one CLI command:

{"mcpServers": {"remove-ai-watermarks": {"command": "uvx", "args": ["remove-ai-watermarks", "mcp"]}}}

See the MCP server guide.

ComfyUI

The separate ComfyUI Remove AI Watermarks package provides nodes for visible removal, detection, region erasing, and invisible removal.

Important limitations

• A missing local signal means unknown, not clean. Proprietary pixel watermarks may remain after metadata has been stripped. What each path leaves behind lists, per command, what may remain and how to check it.
• Visible removal reconstructs a small region. Results depend on the background and selected fill backend.
• Invisible removal changes the whole image and may alter faces, text, or fine detail.
• Visible video removal recognizes the moving Sora 2 wordmark, the current Veo diamond plus legacy Veo text, the Seedance boxed AI label, and the fixed Doubao, Dola, Hailuo AI, Vidu, and Kling AI labels. It does not recognize the older Sora Turbo corner swirl or unregistered layouts from those providers. The classical OpenCV backend can smear structured backgrounds; use MI-GAN or LaMa when recovery quality matters.
• Video SynthID regeneration changes resolution, frame rate, and image detail. The shipped profile is oracle-certified, but no public local decoder can certify an arbitrary output at runtime. Recheck unusually important outputs after provider changes.
• Invisible-watermark removal through the CLI and high-level API requires CUDA. All diffusion profiles refuse any other device at construction rather than falling back to one that cannot run them. The specialized local Paint API, visible removal, metadata stripping and identify run without CUDA.
• Provider watermark systems can change. Validate important outputs with the provider's own verifier when one is available.

The shipped video invisible command uses the certified noise_std=0.15 profile. The companion scripts/video_synthid_sweep.py research harness builds a matched re-encode control plus VAE-regenerated candidates and leaves the verifier verdict blank:

uv run --extra video --extra diffusion python scripts/video_synthid_sweep.py input.mp4 -o sweep/

To score what an output actually cost, use scripts/video_fidelity_probe.py: the engine's own PSNR is measured after the input resize and before final encoding, so only the probe sees the delivered picture.

uv run --extra video python scripts/video_fidelity_probe.py input.mp4 input_clean.mp4

The control must still be SynthID-positive before a negative candidate can count as removal evidence. In the 2026-07-29 two-clip calibration, both matched controls were positive in Gemini's built-in SynthID verifier; the stronger candidate was negative on both carriers, while a weaker candidate was negative on one. A later adversarial follow-up that asked ordinary Gemini to reinterpret the pixel result returned UNAVAILABLE; that follow-up was not a verifier rerun and does not invalidate the built-in verdicts. A 2026-07-31 full-clip check on a public eight-second Veo sample found 0.10 still detected and 0.15 not detected, so 0.15 is now the certified default. The reproducible hashes and verdicts for that full-clip check -- one carrier, two rows -- live in data/evaluations/video-synthid-oracle.csv. The earlier two-clip calibration is narrative only: its verdicts were not recorded in a tracked manifest, so treat the certified default as resting on the 2026-07-31 rows.

Documentation

Start with the documentation index.

• Installation
• CLI guide
• Python API
• Supported signals
• Known limitations
• Scope, safety, and legal notes
• Module internals
• Release and distribution
• Agent skill

Research notes and historical experiments are listed separately in the documentation index. They explain past decisions but do not define the current public API.

Contributing

Install the development environment and run the project gate:

uv sync --frozen --extra dev
bash maintain.sh

See module internals before changing a subsystem with documented invariants. Report vulnerabilities as described in SECURITY.md.

License

Apache 2.0. Copyright 2025-2026 wiltodelta.